In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Input: Consider Input:  In particular, we use Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset for the training Sarcasm Datasets Used in the Experiment
This dataset was created by BIBREF8 . The tweets were downloaded from Twitter using #sarcasm as a marker for sarcastic tweets. It is a monolingual English dataset which consists of a balanced distribution of 50,000 sarcastic tweets and 50,000 non-sarcastic tweets.

Since sarcastic tweets are less frequently used BIBREF8 , we also need to investigate the robustness of the selected features and the model trained on these features on an imbalanced dataset. To this end, we used another English dataset from BIBREF8 . It consists of 25,000 sarcastic tweets and 75,000 non-sarcastic tweets.

We have obtained this dataset from The Sarcasm Detector. It contains 120,000 tweets, out of which 20,000 are sarcastic and 100,000 are non-sarcastic. We randomly sampled 10,000 sarcastic and 20,000 non-sarcastic tweets from the dataset. Visualization of both the original and subset data show similar characteristics. 
 Question: Which benchmark datasets are used?

Output: Semeval 2014 BIBREF34 Twitter Sentiment Analysis Dataset   dataset was created by BIBREF8  English dataset from BIBREF8  dataset from The Sarcasm Detector


Input: Consider Input: We adopt a DNN-based acoustic model BIBREF0 with 11 hidden layers and the alignment used to train the model is derived from a HMM-GMM model trained with SAT criterion. 
 Question: What are the deep learning architectures used in the task?

Output: DNN-based acoustic model BIBREF0


Input: Consider Input: We use 300 Dimensional Glove Common Crawl Embeddings (840B Token) BIBREF11 and fine tune them for the task. 
 Question: What data are the embeddings trained on?


Output: Common Crawl 


